Toxic nanoparticles in Amazon River sediments: Microscopy and satellite-based assessment.

The dynamics of Chlorophyll a (CLA) aggregation in the plant thylakoid membranes have been investigated using coarse-grained molecular dynamics simulations and machine learning approaches at K. The CLA molecules dynamically form and break CLA dimers and higher-order aggregates. The contact lifetime and waiting time distributions of CLA dimers exhibit the coexistence of the fast and slow time scales. The survival probability of CLA dimers follows a non-exponential decay with multiple residence time scales and a time-dependent rate, unlike conventional rate theory, manifesting the emergence of disorder. Markov state modeling using an autoencoder-derived latent representation of pairwise distances among CLAs results in three discrete interconvertible metastable states with different conformational distributions during the aggregation process. The mean first-passage times between Markov states of different conformations are slower than the longest residence time of the dimers. This demonstrates that these transitions represent the slowest kinetic processes during aggregation. Our results provide the underlying mechanism of membrane-bound chlorophyll aggregation, which will have useful applications in photodynamic therapy and artificial photosynthetic materials in the future.

Abstract. The Gabon-Congo upwelling system, located in the southeastern Gulf of Guinea, is a highly productive marine ecosystem influenced by both local and remote physical forcing. This study investigates the seasonal variability of the nitrate budget and biological productivity in this region using a high-resolution (1/36°) coupled physical-biogeochemical simulation with the NEMO-PISCES model. The analysis highlights the relative contributions of physical and biological processes in modulating nitrate concentrations in both the mixed layer and the euphotic zone. Results reveal a semi-annual cycle of nitrate, with two upwelling periods (May–August and December) and two downwelling periods (January–April and October–November). These cycles are primarily driven by the passage of coastal trapped waves (CTWs) forced by equatorial Kelvin waves, inducing vertical thermocline displacements and regulating nitrate availability in the euphotic zone. The nitrate budget analysis shows that the vertical diffusion linked to internal tide and local wind, and vertical advection linked to the CTWs, are the dominant process supplying nitrate to the mixed layer during the main upwelling season. However, near the Congo River mouth (5.5–6° S), the horizontal advection plays a key role, supplying significant amounts of nitrate through the river plume. In the lower euphotic layer, the vertical mixing contributes to the nitrate loss during the upwelling but becomes a source of nitrate during the downwelling periods. The seasonal cycle of the chlorophyll a (CHLa) concentration follows that of nitrate, confirming that the primary production in this region is mainly driven by nitrate availability. The study also highlights the role of the Angola Current in transporting low-nitrate waters from the Equatorial Undercurrent, which influences the nitrate and CHLa balance in the Gabon-Congo upwelling system. These findings provide new insights into the mechanisms governing nutrient dynamics and biological productivity in the Gabon-Congo upwelling system. Understanding these processes is crucial for assessing the impact of climate variability on the regional marine ecosystems and fisheries.

The Ciénaga de la Virgen (Virgen Swamp) is a coastal lagoon in Cartagena de Indias that provides multiple ecosystem services in northern Bolívar. This ecosystem has faced anthropogenic pressure from city growth and improper water resource management, including wastewater and agrochemical discharges. Consequently, environmental authorities must monitor certain sites within the water body and extrapolate the data across its entire expanse. In this study, predictive tools are applied to determine water quality parameters such as chlorophyll-a (CL-a), dissolved oxygen (DO), total suspended solids (TSS), and salinity. This is achieved by correlating traditionally obtained data with the spectral response of medium-resolution satellite images, adjusted using artificial intelligence (AI) algorithms. Support vector machine (SVM) algorithms were used for regression, random forests (RF), and artificial neural networks (ANN), achieving an accuracy of 79% for CL-a, 95% for DO, 89% for TSS, and 96% for salinity. Validation was performed using mean absolute percentage error (MAPE) statistical metrics and root mean square error (RMSE).

Lettuce breeding programs have focused on developing genotypes tolerant to biotic and abiotic stress, with few programs aiming at biofortification to increase the nutritional quality of this leafy vegetable. Biplot analysis remains an important tool in breeding programs, allowing for a clear and integrated visualization of both genotype performance and the importance of the evaluated variables. Thus, this work aimed to evaluate, through biplot analysis, biofortified green and purple lettuce lines in summer and winter. Two experiments were conducted at the Vegetable Experimental Station of the Federal University of Uberlândia, Monte Carmelo campus. Seven lines from the Biofortified and Tropicalized Lettuce Genetic Improvement Program at UFU were evaluated. The experimental design adopted was randomized blocks with three replications, totaling 21 plots. The quantification of chlorophyll a (CoA), chlorophyll b (CoB), total chlorophyll (CoT), carotenoids (CaR), and anthocyanins (AT) was performed using the Multiskan™ FC Microplate Photometer at wavelengths of 645, 652, 663, 470, and 535 nm. The analysis showed that PC1 and PC2 explained 72.44% and 22.57% of the variance, respectively. This study demonstrated that lines L2, L6, and L9 have high potential as candidates in genetic improvement programs focused on biofortification. It also showed that chlorophyll, carotenoid, and anthocyanin levels are strongly influenced by edaphoclimatic conditions, highlighting the importance of genotype-environment interactions.

IntroductionCold stress severely restricts the productivity of Alfalfa (Medicago sativa L.), prompting the evolution of complex resistance mechanisms involving post-translational modifications. In this study, we characterized MsPUB210, a gene encoding a U-box E3 ubiquitin ligase, to elucidate its role in cold adaptation.MethodsPhylogenetic analysis confirmed that MsPUB210 is homologous to Arabidopsis U-box proteins, and gene regulatory network (GRN)-guided protein-protein interaction (PPI) network analysis, together with co-expression investigation, first predicted a potential association between MsPUB210 and the transcription factor MsICE1. This prediction was further corroborated by AlphaFold2 (AF2)-predicted 3D structures of their heteromeric complexes, supporting the biological plausibility of this interaction. We demonstrated via yeast two-hybrid (Y2H) assays that MsPUB210 physically interacts with the transcription factor MsICE1. Physiological and biochemical profiling was quantified by measuring chlorophyll (CHL) content, malondialdehyde (MDA) accumulation, proline (Pro) levels, and the activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD).ResultsHeterologous overexpression of MsPUB210 in Arabidopsis thaliana significantly enhanced cold tolerance, characterized by reduced reactive oxygen species (ROS) accumulation and mitigated lipid peroxidation compared to wild-type plants.DiscussionFurthermore, MsPUB210 was found to upregulate downstream cold-responsive genes, suggesting it functions as a positive regulator within the ICE-CBF-COR signaling cascade. Collectively, these findings highlight the pivotal role of MsPUB210 in ubiquitin-mediated cold signaling and identify it as a promising genetic target for breeding cold-resilient forage crops.

This study develops a Long Short-Term Memory (LSTM) neural network for forecasting water quality parameters at the Wat Makham Station based on data collected from the Chao Phraya River, Thailand, for nine months. The study used IoT sensors to collect real-time values for ten water quality indicators: Turbidity (TURB_NTU), Optical Dissolved Oxygen (HDO), Dissolved Oxygen Saturation (HDO_SAT), Spatial Conductivity (SPCOND), Acidity/Basicity (pH), Total Dissolved Solids (TDS), Salinity (SALINITY), Temperature (TEMP), Chlorophyll (CHL), and Depth (DEPTH). The study identified water quality indicators through the implementation of an LSTM model following application of data cleansing techniques, using mainly the Interquartile Range (IQR) method for outlier detection. The results confirm that prediction accuracy varied across parameters. For stable indicators, very high prediction accuracy was achieved: for pH, MSE = 0.0064, MAPE = 0.89%, RMSE = 0.0800, and RMSPE = 1.12%; for salinity, MSE = 0.0006, MAPE = 10.55%, RMSE = 0.0246, and RMSPE = 41.14%. Temperatures were predicted with high confidence also: MAPE = 2.59% and RMSPE = 3.24%. In contrast, highly volatile parameters were difficult to predict; Turbidity MAPE = 32.87% and RMSPE = 109.22%; Chlorophyll MAPE = 38.64% and RMSPE = 190.15%.

Scad fish ( Decapterus ruselli ) is an important pelagic fishery commodity in the Kendari Waters, but fluctuations in catch are often influenced by oceanographic dynamics. This study analyzed the relationship between oceanographic conditions and the abundance of scad fish based on fishermen's catch per unit effort (CPUE) data from 2022 and satellite- derived parameters, including sea surface temperature (SST), salinity (SSS), chlorophyll-a (CHL-a), and sea surface height (SSH). Spatial-temporal analyses were performed using Pearson correlation. Results revealed distinct seasonal variations with the highest CPUE observed in September (~665 kg trip-1). Significant negative correlations were found between SST (r=-0.36; p=0.041) and SSH (r=-0.30; p=0.048), indicating that lower temperatures and sea surface heights correspond to higher fish abundance reflecting the influence of upwelling processes that enhance nutrient availability and productivity. These findings confirm that physical oceanographic factors are more dominant than chemistry or biology in determining the availability of scad fish. The eastern period (August–October) was identified as the most productive and potential phase for fishing activities. This research provides a scientific basis for season-based fisheries management and oceanographic dynamics, while encouraging the sustainable use of fish resources.

The aquatic environments of main stems in large rivers and their connected lakes exhibit significant disparities under human activities. Fish are crucial for sustaining the structure and function of aquatic ecosystems as high-level predators. This study investigated fish communities in 192 samples from lakes and rivers across the Yangtze river (YR) basin utilizing environmental DNA (eDNA) technology. Additionally, the environmental variable impact on fish biodiversity in these two aquatic environments was uncovered. Herein, we identified approximately 230 fish taxa in this basin, with lakes and rivers comprising both prevalent and habitat-specific species. Water quality played different roles in affecting fish diversity in these two water systems. The geography traits, including Longitude, Latitude, and Altitude, as well as the water traits conductivity (CD), demonstrated the variance in fish diversity and community composition in both rivers and lakes. The human activity factors, including permanganate index (PMI), chlorophyll-a (CHLA), and SiO2, elucidated much more variance in fish diversity and community composition in lakes. These findings suggested that human activity factors exert a more significant influence on fish diversity within lakes compared to rivers. Our outcomes document the complex impacts of water quality on fish diversity in different aquatic habitats of the YR basin and emphasize the distinctive considerations required to protect aquatic biodiversity in this basin. However, it should be noted that eDNA technology provides only a single snapshot of community composition. This method possesses limitations common to all approaches (e.g., detection gaps for certain taxa) as well as inherent biases (such as the difficulty in accurately reflecting the abundance and demographic structure of detected species).

Trifolium repens L. (white clover) is a widely distributed perennial legume, which is regarded as one of the most important forages for its high protein content and excellent palatability. Low temperature limits the distribution and productivity of white clover, thereby reducing its economic returns. WRKY transcription factors are key regulators in stress defense and are involved in multiple abiotic stress responses in plants. In this study, a cold inducible gene named TrWRKY41 was cloned from white clover. The TrWRKY41 protein is predominantly localized in the nucleus and functions as a hydrophilic, acidic protein. Under cold stress, the overexpression plants had significantly higher chlorophyll (CHL) and proline (Pro) contents, significantly increased activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD), and malondialdehyde (MDA) content significantly decreased. Compared to wild-type Arabidopsis thaliana, TrWRKY41-overexpressing plants exhibited better cold tolerance. In addition, target genes downstream of the TrWRKY41 transcription factor were predicted utilizing BLAST alignment and AlphaFold2 (version 0.2.0) software, the expression of six genes, including AtCOR47, AtCOR6.6, and AtABI5, was significantly up-regulated under cold stress. It suggests that TrWRKY41 may enhance cold tolerance in Arabidopsis by activating the ICE-CBF-COR cascade. This study provides candidate genes for research on enhancing the cold tolerance of white clover.

This study aims to model the potential habitat distribution of skipjack tuna (Katsuwonus pelamis) in Fisheries Management Areas (FMA) 713 and 714 using a multi-algorithm Species Distribution Modeling (SDM) approach. Catch logbook data from 2020–2024 were combined with key oceanographic parameters, namely sea surface temperature (SST), chlorophyll-a (CHL), salinity (SAL), sea surface height (SSH), and ocean currents (CUR). Four modeling algorithms (Generalized Additive Model (GAM), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MAXENT), and Support Vector Machine (SVM)) were applied and evaluated using two commonly used metrics, the Area Under Curve (AUC) and True Skill Statistics (TSS). The evaluation results showed that most algorithms performed well, with average AUC values > 0.7 and TSS > 0.5, making them suitable for habitat distribution analysis. Variable importance analysis revealed that SAL and SSH were the dominant factors influencing habitat distribution. The contribution of SAL was most prominent in the MAXENT algorithm (47.47%), while SSH was dominant in MARS (34.23%). Prediction overlays generated spatial habitat suitability index (HSI) maps, with a suitability threshold of ≥ 0.6 indicating optimal habitats. The results indicated that the skipjack tuna habitats are seasonal, being most extensive and stable in the first half of the year (April–July) and at the end of the year (November–December), aligning with the skipjack fishing seasons.

Remote sensing data from satellite platforms were the only available source of information for environmental studies during the COVID-19 lockdown in many regions of the world. We analyzed the spatial variability of representative water indices derived from the Sentinel-2 sensor across six coastal land cover classes along a tourist destination on the North Pacific coast of Mexico. A comparative assessment was conducted between the 2020 lockdown period and the same holiday season in 2019, 2020, and 2022, evaluating the spatial distribution of water indices per coastal class. Principal coordinate analysis of organic content matter (CDOM), Chlorophyll-a (CHLA), and total suspended matter (TSMC2 and TSM_Clear) indices demonstrated clear distinctions in water quality among pre-pandemic (2019), pandemic (2020), and post-pandemic (2021−2022) periods. Canonical analysis of principal coordinates during the lockdown year revealed two key patterns: (1) sewage and harbor areas displayed a significant decrease in CHLA levels alongside elevated TSMC2, while (2) mangrove forest exhibited markedly reduced CDOM in post-pandemic years. Distance-based redundancy analysis further showed interannual variability across coastal zones, while the pandemic year (2020) was particularly distinguished by diminished CDOM in tourist and industrial areas. The high-resolution (10 m/pixel) and revisit time (5 days) of Sentinel-2 data was invaluable for monitoring water quality dynamics during the COVID-19 lockdown.

Understanding habitat suitability for Bullet Tuna (Auxis rochei) is crucial for sustainable fisheries management, yet limited studies have integrated geospatial modeling for this purpose, particularly in the Banda Sea, part of the Indonesian Coral Triangle. This study employs remote sensing and geospatial analysis to predict Bullet Tuna habitat suitability and fishing seasonality. Key oceanographic parameters, including sea surface temperature (SST), chlorophyll-a (CHL), sea surface salinity (SSS), sea surface height (SSH), and ocean current velocity (CUR), were derived from MODIS and Copernicus satellite products and integrated with catch per unit effort (CPUE) records from 2018 to 2022. The MaxEnt model was selected for its robustness in predicting species distribution from presence-only data, while remote sensing enables continuous monitoring of dynamic oceanographic conditions across broad spatial scales. The Fishing Season Index (FSI) was applied to determine seasonal fishing patterns, and validation was conducted using 2023 fishing data to verify model predictions. Results indicate that SSS and SSH are the most significant determinants of habitat suitability, with optimal ranges of 33.50–34.00 psu and 0.72–0.74 m, respectively. The peak fishing season occurs from December to March, coinciding with high habitat suitability values and elevated CPUE. These findings offer actionable insights for dynamic fisheries management, marine protected area planning, and real-time monitoring of Bullet Tuna habitats in the Banda Sea and broader Coral Triangle region.

This study presents an innovative predictive monitoring framework that integrates the Internet of Things (IoT) with advanced machine learning (ML) techniques to model the relationship between oxidized nitrate (NOX)—employed as the sole predictor—and chlorophyll a (CHLA), a key proxy for algal biomass. By utilising a single optimally selected parameter, the approach significantly reduces sensor deployment complexity and instrumentation costs, while minimising data acquisition and computational requirements. Logarithmic and Yeo-Johnson transformations were applied to the predictor and target variables, respectively, to address distributional skewness and enhance variance homogeneity. An optimised Random Forest model demonstrated strong predictive performance, achieving a coefficient of determination (R2) of 0.8392 and low error metrics (MSE = 0.1630; RMSE = 0.4037; MAE = 0.2774). These results highlight the framework's efficacy and scalability in delivering resource-efficient, data-light predictive systems capable of real-time, automated monitoring via an IoT-enabled architecture. The study underscores the potential of combining IoT infrastructures with machine learning to develop computationally efficient, scalable solutions for the intelligent management of complex dynamic systems.

Global climate change poses a significant management challenge for vital transboundary resources like the Pacific saury (Cololabis saira). To address this, we developed an innovative framework that uses high-resolution Automatic Identification System (AIS) data and deep learning to define species distribution, which then informs a robust Ensemble Species Distribution Model (ESDM). The model (TSS > 0.89, AUC > 0.97) identifies sea surface temperature (SST) and chlorophyll-a (CHL) as key habitat drivers. Projections under future climate scenarios reveal two critical threats: (1) a continuous northeastward migration of the habitat's centroid, exceeding 400 km by 2100 under a high-emission SSP5-8.5 scenario, and (2) a drastic contraction of highly suitable habitat (suitability > 0.8), shrinking by up to 94% under the high-emission SSP3-7.0 scenario. By directly linking key oceanographic features to these climate-driven risks, this study delivers an essential scientific decision-support tool for management bodies like the North Pacific Fisheries Commission (NPFC) to develop climate-adaptive strategies.

ABSTRACTThe various anthropogenic activities around the river in Pekalongan can influence the dynamics of the estuary. The estuary is important in fisheries, particularly small pelagic fish, related to plankton abundance and aquatic fertility. The recent tendency of the coastal development area raises the establishment of small‐scale industries, textile industries, and ecotourism, which carry a significant amount of disposal waste as well as organic matter. The tidal flooding influenced the dynamic of organic matter in Pekalongan, which increased yearly due to climate change. This study aims to determine the content and distribution of nitrate, phosphate, total suspended solids (TSS), and chlorophyll‐a (CHA) collected from 2021 to 2024 at four rivers in Pekalongan Regency based on a geospatial approach. The study results showed that the CHA contents from 2021 to 2024 varied from 0.196 to 2.473 µg/L, 0.022 to 0.379 µg/L, 0.002 to 2.414 µg/L, and 0.21 to 3.083 µg/L; TSS contents from 2021 to 2024 varied from 4 to 26 mg/L, 64.7 to 149 mg/L, 16.3 to 214 mg/L, and 19 to 299 mg/L; nitrate contents from 2023 to 2024 varied from 0.004 to 1.31 mg/L and 0.004 to 0.959 mg/L; and phosphate contents from 2023 to 2024 ranged from 0.003 to 0.767 mg/L and 0.001 to 1.017 mg/L. The concentration of CHA and TSS varied each year. However, the existing pattern shows an increase in TSS concentration and a decrease in CHA values, which indicates a reduction in water quality. The nitrate concentration tends to decrease, and the phosphate concentration has increased recently. The dynamic distribution of organic matter occurs due to the influence of tidal currents, longshore currents, and the east monsoon wind. The data collection showed the tendency for environmental quality degradation due to the limited carrying capacity and shortage of remediation.

Chlorophyll-a (CL-a) and chlorophyll-b (CL-b) are major chlorophyll found in green plants. Determining the CL-a, CL-b, and total chlorophyll (TCL) contents is important to guide crop growth. However, the widely used portable chlorophyll detectors, such as SPAD-502, are limited to measuring relative chlorophyll content and can't measure the contents of CL-a, CL-b, and TCL. It was reported that the chlorophyll content was related to the color indices of leaves, which inspired us to develop a portable detector that can non-destructively measure the contents of CL-a, CL-b, and TCL based on a color sensor. Therefore, the world's major crops, i.e., wheat and maize, are used as samples to develop a handheld chlorophyll content detector for leaves in this study. The detector was mainly composed of a microcontroller, RGB sensor, light source, and power management module, etc. The software, developed in Keil μVision5, was composed of a main function and several sub-functions, such as the leaf color collection sub-function, data processing sub-function, key sub-function, and display sub-function. The relationships of CL-a, CL-b, and TCL contents with the color features of wheat and maize leaves were analyzed. The results showed that these chlorophyll contents had high correlations with B (blue), B' (blue light intensity), H (hue), S (saturation), and V (value) and can be expressed by five-variable equations. Compared with the chlorophyll contents measured by the traditional spectrophotometry method, the root-mean-square errors of the developed detector were 0.269 mg/g, 0.089 mg/g, and 0.350 mg/g for CL-a, CL-b, and TCL contents, respectively. The small size, light weight, and quick measurement (about 2 s) make the detector will be important for instructing crop breeding, fertilization, and other management.

Fresh fruit bunches (FFB) harvested between 19 and 24 weeks after anthesis (WAA) were processed in a laboratory to extract crude palm oil (CPO).The CPO was mechanically extracted from oil palm fruits at various stages of maturity and analysed for its physico-chemical properties, including deterioration of bleachability index (DOBI), carotene content, total chloride content (TCC) and chlorophyll (CHL) pigments.Underripe fruits at 19 WAA had a higher moisture content (MC) (72.69%) compared to ripe fruits at 23 WAA (26.84%).As the fruits fully matured, the oil content (OC) increased, exceeding 40.00% of the total fruit mass.The quality of CPO extracted from underripe fruits was characterised by lower carotene content and DOBI values (175.00 mg kg -1 and 2.19, respectively), but higher levels of CHL pigments (5.38 mg kg -1 ) and TCC (7.00 mg kg -1 ).In contrast, the CPO from fully developed fruits exhibited higher carotene content and DOBI values (762.00 mg kg -1 and 3.45, respectively), while CHL pigments and TCC decreased.Good quality CPO is identified by a higher DOBI value and lower CHL pigments and TCC.A higher DOBI value indicates easier bleaching and refining, while lower CHL pigment levels contribute to better oxidative stability.

Oceanographic parameters are related to the distribution of fish in the waters. The purpose of this study was to analyze the distribution trends of sea surface temperature (SST) and chlorophyll-a (CHL) and to determine the spatial-temporal correlation with the catch of yellowfin tuna. This research took place in WPP 573 and 713 between the north and south of the Alas Strait. Remote sensing methods and statistical regression analysis for SST and CHL in 2023 over 12 months. The penetration results of Aqua MODIS in 2023 show the highest fluctuation SST trend in December (30.71°C), the lowest in August (27.3°C). The highest CHL distribution occurred in December (37.81 mg/m3), and the lowest in February (0.66 mg/m3). The results of linear regression analysis between SSTs; CHL; SST and CHL, for yellowfin tuna fishing, obtained a correlation coefficient (r) of 0.421 each; 0,476; 0.623. Conclusions: 1). The relationship between SST and CHL with yellowfin tuna is in the strong category; 2). The fluctuating catch of yellowfin tuna causes the CHL and SST trends to change every month. CHL and SST values were highest in December, but they did not have a major effect on catches.

Soil salinity is an essential threat to the productivity and quality of vegeta ble crops. Tomatoes are the major vegetable, and their response to salinity and salinity management strategies has been widely studied. However, the studies evaluating alleviation strategies at the field level are meagre. A field experiment was conducted to research zinc nutrition's effect on salinity stress alleviation in two tomato cultivars. The experiment consisted of two cultivars (PKM 1 and Sivam) and four levels of zinc application as ZnSO4 (0, 25, 50 and 75 kg ha-1) with three replications. The growth, yield, physiologi cal and biochemical parameters were recorded during harvest. Results showed that among the cultivars, Sivam recorded the higher plant height (87.9 cm), number of branches (11.2), dry-matter production (139.6 g plant-1), number of fruits (85.1) and fruit yield (83.2 t ha-1). Growth trends and yield attributes were observed under salinity stress with increasing zinc applica tion levels. The highest fruit yield was recorded in Sivam and PKM 1 with ZnSO4 application at the rate of 75 kg ha-1. Applying ZnSO4 at the rate of 75 kg ha-1 recorded higher fruit yields of 33.7 and 92.8 t ha-1 in cultivars PKM 1 and Sivam, respectively. The percent increase in yield in cultivar PKM 1 and Sivam over control was 27.1 and 27.8, respectively. Nutrient availability and uptake increased with ZnSO4 application and was the highest at 75 kg ha-1. Physiological parameters viz. leaf area, specific leaf area, total chloro phyll content, membrane stability index, and chlorophyll stability index were improved with ZnSO4 application. Proline content was not affected by the ZnSO4 application. The activity of catalase and superoxide dismutase was increased by applying ZnSO4. The correlation of ZnSO4 application with the growth, yield, physiological and biochemical parameters shows that zinc sulphate application positively affects all the factors affected by soil salinity. Hence, applying ZnSO4 at the rate of 75 kg ha-1 can be compulsorily recommended for tomato growers of the region to overcome zinc deficiency and boost the fruit yield under saline conditions.